Overview

Dataset statistics

Number of variables18
Number of observations1000
Missing cells158
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory470.7 KiB
Average record size in memory482.0 B

Variable types

Text1
Numeric8
Categorical7
DateTime2

Alerts

category is highly overall correlated with priceHigh correlation
price is highly overall correlated with categoryHigh correlation
age has 49 (4.9%) missing valuesMissing
annual_income has 50 (5.0%) missing valuesMissing
loyalty_score has 59 (5.9%) missing valuesMissing
transaction_id has unique valuesUnique

Reproduction

Analysis started2026-02-23 11:06:51.890622
Analysis finished2026-02-23 11:07:04.086841
Duration12.2 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

transaction_id
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
2026-02-23T16:37:04.829967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowT00001
2nd rowT00002
3rd rowT00003
4th rowT00004
5th rowT00005
ValueCountFrequency (%)
t000011
 
0.1%
t000021
 
0.1%
t000031
 
0.1%
t000041
 
0.1%
t000051
 
0.1%
t000061
 
0.1%
t000071
 
0.1%
t000081
 
0.1%
t000091
 
0.1%
t000101
 
0.1%
Other values (990)990
99.0%
2026-02-23T16:37:05.813555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
02299
38.3%
T1000
16.7%
1301
 
5.0%
2300
 
5.0%
3300
 
5.0%
4300
 
5.0%
5300
 
5.0%
6300
 
5.0%
7300
 
5.0%
8300
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)6000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02299
38.3%
T1000
16.7%
1301
 
5.0%
2300
 
5.0%
3300
 
5.0%
4300
 
5.0%
5300
 
5.0%
6300
 
5.0%
7300
 
5.0%
8300
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02299
38.3%
T1000
16.7%
1301
 
5.0%
2300
 
5.0%
3300
 
5.0%
4300
 
5.0%
5300
 
5.0%
6300
 
5.0%
7300
 
5.0%
8300
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02299
38.3%
T1000
16.7%
1301
 
5.0%
2300
 
5.0%
3300
 
5.0%
4300
 
5.0%
5300
 
5.0%
6300
 
5.0%
7300
 
5.0%
8300
 
5.0%

customer_id
Real number (ℝ)

Distinct637
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean507.486
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-23T16:37:06.026189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile52.95
Q1267.75
median521.5
Q3744.25
95-th percentile945.15
Maximum1000
Range999
Interquartile range (IQR)476.5

Descriptive statistics

Standard deviation286.79851
Coefficient of variation (CV)0.5651358
Kurtosis-1.1686127
Mean507.486
Median Absolute Deviation (MAD)236.5
Skewness-0.07015154
Sum507486
Variance82253.383
MonotonicityNot monotonic
2026-02-23T16:37:06.268849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7075
 
0.5%
5705
 
0.5%
6425
 
0.5%
6515
 
0.5%
7455
 
0.5%
3594
 
0.4%
1024
 
0.4%
6334
 
0.4%
4784
 
0.4%
8354
 
0.4%
Other values (627)955
95.5%
ValueCountFrequency (%)
13
0.3%
21
 
0.1%
31
 
0.1%
52
0.2%
62
0.2%
71
 
0.1%
82
0.2%
92
0.2%
101
 
0.1%
121
 
0.1%
ValueCountFrequency (%)
10002
0.2%
9963
0.3%
9952
0.2%
9941
 
0.1%
9932
0.2%
9921
 
0.1%
9911
 
0.1%
9891
 
0.1%
9861
 
0.1%
9851
 
0.1%

product_id
Real number (ℝ)

Distinct100
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.406
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-23T16:37:06.485091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q125
median50
Q376
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.223538
Coefficient of variation (CV)0.57976309
Kurtosis-1.226461
Mean50.406
Median Absolute Deviation (MAD)26
Skewness0.0055117714
Sum50406
Variance854.01518
MonotonicityNot monotonic
2026-02-23T16:37:06.721105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1817
 
1.7%
5117
 
1.7%
9116
 
1.6%
8516
 
1.6%
416
 
1.6%
4616
 
1.6%
1115
 
1.5%
8614
 
1.4%
714
 
1.4%
7414
 
1.4%
Other values (90)845
84.5%
ValueCountFrequency (%)
110
1.0%
29
0.9%
38
0.8%
416
1.6%
59
0.9%
68
0.8%
714
1.4%
810
1.0%
910
1.0%
1012
1.2%
ValueCountFrequency (%)
10013
1.3%
997
0.7%
9812
1.2%
979
0.9%
9610
1.0%
957
0.7%
9410
1.0%
9312
1.2%
9210
1.0%
9116
1.6%

quantity
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
3
261 
2
250 
4
249 
1
240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
3261
26.1%
2250
25.0%
4249
24.9%
1240
24.0%

Length

2026-02-23T16:37:07.195273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:37:07.385270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3261
26.1%
2250
25.0%
4249
24.9%
1240
24.0%

Most occurring characters

ValueCountFrequency (%)
3261
26.1%
2250
25.0%
4249
24.9%
1240
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3261
26.1%
2250
25.0%
4249
24.9%
1240
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3261
26.1%
2250
25.0%
4249
24.9%
1240
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3261
26.1%
2250
25.0%
4249
24.9%
1240
24.0%
Distinct599
Distinct (%)59.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2022-01-01 00:00:00
Maximum2024-06-17 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-23T16:37:07.630393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:07.873472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

purchased
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
1
695 
0
305 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1695
69.5%
0305
30.5%

Length

2026-02-23T16:37:08.111006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:37:08.237338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1695
69.5%
0305
30.5%

Most occurring characters

ValueCountFrequency (%)
1695
69.5%
0305
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1695
69.5%
0305
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1695
69.5%
0305
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1695
69.5%
0305
30.5%

age
Real number (ℝ)

Missing 

Distinct57
Distinct (%)6.0%
Missing49
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean46.361725
Minimum18
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-23T16:37:08.382092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q132
median47
Q361
95-th percentile73
Maximum74
Range56
Interquartile range (IQR)29

Descriptive statistics

Standard deviation16.711785
Coefficient of variation (CV)0.36046513
Kurtosis-1.1649677
Mean46.361725
Median Absolute Deviation (MAD)14
Skewness-0.0012251337
Sum44090
Variance279.28375
MonotonicityNot monotonic
2026-02-23T16:37:08.565217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7430
 
3.0%
1827
 
2.7%
2626
 
2.6%
3425
 
2.5%
4025
 
2.5%
5125
 
2.5%
4124
 
2.4%
4924
 
2.4%
5023
 
2.3%
5623
 
2.3%
Other values (47)699
69.9%
(Missing)49
 
4.9%
ValueCountFrequency (%)
1827
2.7%
1915
1.5%
2011
1.1%
2119
1.9%
2210
 
1.0%
2320
2.0%
2410
 
1.0%
2516
1.6%
2626
2.6%
2721
2.1%
ValueCountFrequency (%)
7430
3.0%
7321
2.1%
7214
1.4%
7117
1.7%
7015
1.5%
6918
1.8%
6816
1.6%
6721
2.1%
6619
1.9%
6521
2.1%

gender
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size60.6 KiB
Male
364 
Other
331 
Female
305 

Length

Max length6
Median length5
Mean length4.941
Min length4

Characters and Unicode

Total characters4941
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowMale
4th rowMale
5th rowOther

Common Values

ValueCountFrequency (%)
Male364
36.4%
Other331
33.1%
Female305
30.5%

Length

2026-02-23T16:37:08.818414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:37:08.981606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male364
36.4%
other331
33.1%
female305
30.5%

Most occurring characters

ValueCountFrequency (%)
e1305
26.4%
a669
13.5%
l669
13.5%
M364
 
7.4%
O331
 
6.7%
t331
 
6.7%
h331
 
6.7%
r331
 
6.7%
F305
 
6.2%
m305
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1305
26.4%
a669
13.5%
l669
13.5%
M364
 
7.4%
O331
 
6.7%
t331
 
6.7%
h331
 
6.7%
r331
 
6.7%
F305
 
6.2%
m305
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1305
26.4%
a669
13.5%
l669
13.5%
M364
 
7.4%
O331
 
6.7%
t331
 
6.7%
h331
 
6.7%
r331
 
6.7%
F305
 
6.2%
m305
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1305
26.4%
a669
13.5%
l669
13.5%
M364
 
7.4%
O331
 
6.7%
t331
 
6.7%
h331
 
6.7%
r331
 
6.7%
F305
 
6.2%
m305
 
6.2%

city
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size62.2 KiB
Rajkot
179 
Delhi
173 
Ahmedabad
173 
Mumbai
170 
Vadodara
158 

Length

Max length9
Median length8
Mean length6.515
Min length5

Characters and Unicode

Total characters6515
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRajkot
2nd rowVadodara
3rd rowMumbai
4th rowMumbai
5th rowDelhi

Common Values

ValueCountFrequency (%)
Rajkot179
17.9%
Delhi173
17.3%
Ahmedabad173
17.3%
Mumbai170
17.0%
Vadodara158
15.8%
Surat147
14.7%

Length

2026-02-23T16:37:09.144451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:37:09.305516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
rajkot179
17.9%
delhi173
17.3%
ahmedabad173
17.3%
mumbai170
17.0%
vadodara158
15.8%
surat147
14.7%

Most occurring characters

ValueCountFrequency (%)
a1316
20.2%
d662
 
10.2%
e346
 
5.3%
h346
 
5.3%
m343
 
5.3%
b343
 
5.3%
i343
 
5.3%
o337
 
5.2%
t326
 
5.0%
u317
 
4.9%
Other values (10)1836
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)6515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1316
20.2%
d662
 
10.2%
e346
 
5.3%
h346
 
5.3%
m343
 
5.3%
b343
 
5.3%
i343
 
5.3%
o337
 
5.2%
t326
 
5.0%
u317
 
4.9%
Other values (10)1836
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1316
20.2%
d662
 
10.2%
e346
 
5.3%
h346
 
5.3%
m343
 
5.3%
b343
 
5.3%
i343
 
5.3%
o337
 
5.2%
t326
 
5.0%
u317
 
4.9%
Other values (10)1836
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1316
20.2%
d662
 
10.2%
e346
 
5.3%
h346
 
5.3%
m343
 
5.3%
b343
 
5.3%
i343
 
5.3%
o337
 
5.2%
t326
 
5.0%
u317
 
4.9%
Other values (10)1836
28.2%

annual_income
Real number (ℝ)

Missing 

Distinct606
Distinct (%)63.8%
Missing50
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean1223201.7
Minimum122258
Maximum2499544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-23T16:37:09.543498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum122258
5-th percentile232582.35
Q1635492
median1157025
Q31810788
95-th percentile2310970
Maximum2499544
Range2377286
Interquartile range (IQR)1175296

Descriptive statistics

Standard deviation673620.26
Coefficient of variation (CV)0.55070253
Kurtosis-1.2103419
Mean1223201.7
Median Absolute Deviation (MAD)584766
Skewness0.12480239
Sum1.1620416 × 109
Variance4.5376426 × 1011
MonotonicityNot monotonic
2026-02-23T16:37:09.741393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18451145
 
0.5%
1595655
 
0.5%
2939905
 
0.5%
18002995
 
0.5%
21292514
 
0.4%
14922144
 
0.4%
15125324
 
0.4%
9483244
 
0.4%
19126504
 
0.4%
18418814
 
0.4%
Other values (596)906
90.6%
(Missing)50
 
5.0%
ValueCountFrequency (%)
1222583
0.3%
1223191
 
0.1%
1250532
 
0.2%
1298982
 
0.2%
1308091
 
0.1%
1324222
 
0.2%
1335571
 
0.1%
1400721
 
0.1%
1550202
 
0.2%
1595655
0.5%
ValueCountFrequency (%)
24995441
0.1%
24917331
0.1%
24806232
0.2%
24746702
0.2%
24587772
0.2%
24541932
0.2%
24512822
0.2%
24346442
0.2%
24227602
0.2%
24225242
0.2%
Distinct547
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2018-01-02 00:00:00
Maximum2023-06-22 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-23T16:37:09.937597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:10.204750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

loyalty_score
Real number (ℝ)

Missing 

Distinct582
Distinct (%)61.8%
Missing59
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean50.704166
Minimum1.4
Maximum99.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-23T16:37:10.459309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile6.23
Q126.53
median49.83
Q376.28
95-th percentile93.88
Maximum99.73
Range98.33
Interquartile range (IQR)49.75

Descriptive statistics

Standard deviation28.327537
Coefficient of variation (CV)0.55868264
Kurtosis-1.1759913
Mean50.704166
Median Absolute Deviation (MAD)24.9
Skewness0.013455043
Sum47712.62
Variance802.44938
MonotonicityNot monotonic
2026-02-23T16:37:10.717802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.235
 
0.5%
56.055
 
0.5%
71.535
 
0.5%
39.515
 
0.5%
76.455
 
0.5%
88.665
 
0.5%
37.585
 
0.5%
45.074
 
0.4%
91.114
 
0.4%
28.24
 
0.4%
Other values (572)894
89.4%
(Missing)59
 
5.9%
ValueCountFrequency (%)
1.41
 
0.1%
1.621
 
0.1%
1.712
0.2%
1.851
 
0.1%
2.361
 
0.1%
2.383
0.3%
2.392
0.2%
2.431
 
0.1%
2.471
 
0.1%
2.982
0.2%
ValueCountFrequency (%)
99.731
 
0.1%
99.521
 
0.1%
99.361
 
0.1%
99.051
 
0.1%
98.821
 
0.1%
98.782
0.2%
98.611
 
0.1%
98.532
0.2%
98.453
0.3%
98.442
0.2%

is_active
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
1
799 
0
201 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1799
79.9%
0201
 
20.1%

Length

2026-02-23T16:37:10.934162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:37:11.054822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1799
79.9%
0201
 
20.1%

Most occurring characters

ValueCountFrequency (%)
1799
79.9%
0201
 
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1799
79.9%
0201
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1799
79.9%
0201
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1799
79.9%
0201
 
20.1%

category
Categorical

High correlation 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size62.0 KiB
Toys
250 
Home
197 
Groceries
186 
Clothing
154 
Books
108 

Length

Max length11
Median length9
Mean length6.389
Min length4

Characters and Unicode

Total characters6389
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGroceries
2nd rowBooks
3rd rowElectronics
4th rowClothing
5th rowHome

Common Values

ValueCountFrequency (%)
Toys250
25.0%
Home197
19.7%
Groceries186
18.6%
Clothing154
15.4%
Books108
10.8%
Electronics105
10.5%

Length

2026-02-23T16:37:11.203941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:37:11.353566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
toys250
25.0%
home197
19.7%
groceries186
18.6%
clothing154
15.4%
books108
10.8%
electronics105
10.5%

Most occurring characters

ValueCountFrequency (%)
o1108
17.3%
e674
10.5%
s649
10.2%
r477
 
7.5%
i445
 
7.0%
c396
 
6.2%
t259
 
4.1%
n259
 
4.1%
l259
 
4.1%
T250
 
3.9%
Other values (10)1613
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)6389
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1108
17.3%
e674
10.5%
s649
10.2%
r477
 
7.5%
i445
 
7.0%
c396
 
6.2%
t259
 
4.1%
n259
 
4.1%
l259
 
4.1%
T250
 
3.9%
Other values (10)1613
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6389
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1108
17.3%
e674
10.5%
s649
10.2%
r477
 
7.5%
i445
 
7.0%
c396
 
6.2%
t259
 
4.1%
n259
 
4.1%
l259
 
4.1%
T250
 
3.9%
Other values (10)1613
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6389
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1108
17.3%
e674
10.5%
s649
10.2%
r477
 
7.5%
i445
 
7.0%
c396
 
6.2%
t259
 
4.1%
n259
 
4.1%
l259
 
4.1%
T250
 
3.9%
Other values (10)1613
25.2%

price
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7988.055
Minimum174.87
Maximum73715.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-23T16:37:11.575747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum174.87
5-th percentile579.59
Q11250.97
median2617.55
Q35919.6325
95-th percentile35068.14
Maximum73715.1
Range73540.23
Interquartile range (IQR)4668.6625

Descriptive statistics

Standard deviation12775.886
Coefficient of variation (CV)1.5993739
Kurtosis8.1173278
Mean7988.055
Median Absolute Deviation (MAD)1568.3
Skewness2.7350137
Sum7988055
Variance1.6322327 × 108
MonotonicityNot monotonic
2026-02-23T16:37:11.801955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1049.2517
 
1.7%
1250.9717
 
1.7%
579.5916
 
1.6%
28165.6516
 
1.6%
1696.1316
 
1.6%
5893.3516
 
1.6%
1509.9915
 
1.5%
5620.9814
 
1.4%
1388.4114
 
1.4%
1192.114
 
1.4%
Other values (90)845
84.5%
ValueCountFrequency (%)
174.875
 
0.5%
498.0610
1.0%
500.5711
1.1%
509.1512
1.2%
529.999
0.9%
579.5916
1.6%
596.8412
1.2%
630.4810
1.0%
663.7110
1.0%
698.74
 
0.4%
ValueCountFrequency (%)
73715.16
 
0.6%
60872.0912
1.2%
57496.38
0.8%
43365.657
0.7%
42704.0811
1.1%
35068.147
0.7%
28480.848
0.8%
28275.99
0.9%
28165.6516
1.6%
23609.459
0.9%

discount
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size57.3 KiB
5
261 
15
242 
10
173 
0
166 
20
158 

Length

Max length2
Median length2
Mean length1.573
Min length1

Characters and Unicode

Total characters1573
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row0
3rd row0
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5261
26.1%
15242
24.2%
10173
17.3%
0166
16.6%
20158
15.8%

Length

2026-02-23T16:37:12.010015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T16:37:12.145214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5261
26.1%
15242
24.2%
10173
17.3%
0166
16.6%
20158
15.8%

Most occurring characters

ValueCountFrequency (%)
5503
32.0%
0497
31.6%
1415
26.4%
2158
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5503
32.0%
0497
31.6%
1415
26.4%
2158
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5503
32.0%
0497
31.6%
1415
26.4%
2158
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5503
32.0%
0497
31.6%
1415
26.4%
2158
 
10.0%

stock_qty
Real number (ℝ)

Distinct87
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.195
Minimum7
Maximum497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-23T16:37:12.339257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile29
Q1140
median246
Q3388
95-th percentile463
Maximum497
Range490
Interquartile range (IQR)248

Descriptive statistics

Standard deviation139.21431
Coefficient of variation (CV)0.5498304
Kurtosis-1.137067
Mean253.195
Median Absolute Deviation (MAD)113
Skewness0.050993246
Sum253195
Variance19380.624
MonotonicityNot monotonic
2026-02-23T16:37:12.801060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16629
 
2.9%
7928
 
2.8%
40826
 
2.6%
16324
 
2.4%
2924
 
2.4%
43321
 
2.1%
12721
 
2.1%
27319
 
1.9%
35918
 
1.8%
44118
 
1.8%
Other values (77)772
77.2%
ValueCountFrequency (%)
78
 
0.8%
1113
1.3%
1210
 
1.0%
139
 
0.9%
157
 
0.7%
2924
2.4%
305
 
0.5%
409
 
0.9%
609
 
0.9%
7928
2.8%
ValueCountFrequency (%)
49715
1.5%
48712
1.2%
4788
0.8%
4707
 
0.7%
4697
 
0.7%
46310
1.0%
4567
 
0.7%
4549
0.9%
44510
1.0%
44118
1.8%

rating
Real number (ℝ)

Distinct34
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7413
Minimum1
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-23T16:37:13.004864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.1
Q11.7
median2.6
Q33.3
95-th percentile4.8
Maximum4.9
Range3.9
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.1616341
Coefficient of variation (CV)0.42375299
Kurtosis-0.94774691
Mean2.7413
Median Absolute Deviation (MAD)0.8
Skewness0.39909837
Sum2741.3
Variance1.3493937
MonotonicityNot monotonic
2026-02-23T16:37:13.214503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3.268
 
6.8%
2.253
 
5.3%
1.552
 
5.2%
2.951
 
5.1%
1.646
 
4.6%
1.943
 
4.3%
4.843
 
4.3%
2.841
 
4.1%
341
 
4.1%
1.437
 
3.7%
Other values (24)525
52.5%
ValueCountFrequency (%)
129
2.9%
1.127
2.7%
1.223
2.3%
1.319
 
1.9%
1.437
3.7%
1.552
5.2%
1.646
4.6%
1.724
2.4%
1.820
 
2.0%
1.943
4.3%
ValueCountFrequency (%)
4.935
3.5%
4.843
4.3%
4.717
 
1.7%
4.626
2.6%
4.59
 
0.9%
4.435
3.5%
4.316
 
1.6%
433
3.3%
3.98
 
0.8%
3.610
 
1.0%

Interactions

2026-02-23T16:37:01.489464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:52.774602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:53.585900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:54.370131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:55.211103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:56.390294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:58.183248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:59.986219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:01.684418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:52.877663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:53.704596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:54.448371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:55.355334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:56.582378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:58.387764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:00.185875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:01.898825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:52.954985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:53.783619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:54.553439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:55.498241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:56.782872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:58.774714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:00.362502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:02.128535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:53.056320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:53.894478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:54.630410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:55.706755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:57.005433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:58.969841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:00.538040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:02.365424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:53.144811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:53.977944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:54.707788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:55.890488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:57.223540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:59.195350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:00.719713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:02.569917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:53.259137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:54.055033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:54.786180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:55.994461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:57.442359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:59.399973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:00.906349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:02.780945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:53.365076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:54.187516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:54.874845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:56.107584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:57.668632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:59.584572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:01.102194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:03.001059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:53.457953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:54.281597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:55.001439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:56.197560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:57.937384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:36:59.777379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T16:37:01.283518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-23T16:37:13.407725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageannual_incomecategorycitycustomer_iddiscountgenderis_activeloyalty_scorepriceproduct_idpurchasedquantityratingstock_qty
age1.000-0.0250.0260.1130.0670.0000.1180.0700.092-0.0340.0300.0290.0000.002-0.003
annual_income-0.0251.0000.0000.111-0.0070.0000.1240.1380.0410.000-0.0460.0660.000-0.005-0.023
category0.0260.0001.0000.0000.0350.2090.0300.0000.0450.5080.2540.0000.0000.2700.260
city0.1130.1110.0001.0000.0940.0240.0680.0430.1010.0090.0290.0070.0000.0290.000
customer_id0.067-0.0070.0350.0941.0000.0300.1020.0450.0050.0040.0040.0250.0000.007-0.029
discount0.0000.0000.2090.0240.0301.0000.0510.0320.0640.2480.2530.0000.0000.2980.254
gender0.1180.1240.0300.0680.1020.0511.0000.0330.0950.0000.0000.0510.0000.0000.000
is_active0.0700.1380.0000.0430.0450.0320.0331.0000.1930.0000.0000.0390.0000.0600.027
loyalty_score0.0920.0410.0450.1010.0050.0640.0950.1931.000-0.0370.0530.0000.0610.012-0.011
price-0.0340.0000.5080.0090.0040.2480.0000.000-0.0371.000-0.0200.0140.000-0.0030.024
product_id0.030-0.0460.2540.0290.0040.2530.0000.0000.053-0.0201.0000.1220.000-0.1940.151
purchased0.0290.0660.0000.0070.0250.0000.0510.0390.0000.0140.1221.0000.0000.0000.000
quantity0.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0001.0000.0000.000
rating0.002-0.0050.2700.0290.0070.2980.0000.0600.012-0.003-0.1940.0000.0001.0000.056
stock_qty-0.003-0.0230.2600.000-0.0290.2540.0000.027-0.0110.0240.1510.0000.0000.0561.000

Missing values

2026-02-23T16:37:03.342600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-23T16:37:03.649853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-23T16:37:03.954644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

transaction_idcustomer_idproduct_idquantitypurchase_datepurchasedagegendercityannual_incomesignup_dateloyalty_scoreis_activecategorypricediscountstock_qtyrating
0T000013251142023-12-24044.0OtherRajkot864504.02018-07-1046.901Groceries1509.9951562.5
1T000025802442022-01-25152.0OtherVadodaraNaN2020-04-11NaN1Books2901.8104363.3
2T000033437622023-06-12126.0MaleMumbai266007.02022-05-2839.171Electronics23392.8701861.6
3T000045702222022-07-01045.0MaleMumbaiNaN2020-08-1771.531Clothing2891.7851691.9
4T000056455212023-05-14026.0OtherDelhi1076659.02023-04-0320.480Home15124.3752371.4
5T000065953042024-04-20163.0FemaleDelhi321927.02023-04-2246.331Groceries1889.88154872.0
6T000073463432023-10-03020.0FemaleMumbai1670159.02022-03-284.261Clothing2850.5953002.8
7T000083593822022-02-11026.0OtherSurat1512532.02019-06-1277.571Home5816.7954334.9
8T000096258832023-04-26156.0FemaleSurat964925.02023-06-1596.191Toys2144.89152584.7
9T000103717012024-04-27170.0FemaleAhmedabad1330870.02019-01-2017.741Clothing2516.6615152.2
transaction_idcustomer_idproduct_idquantitypurchase_datepurchasedagegendercityannual_incomesignup_dateloyalty_scoreis_activecategorypricediscountstock_qtyrating
990T009918894612022-08-30119.0FemaleRajkot1649449.02020-11-1126.551Toys5893.35203361.0
991T009923163242022-10-21018.0MaleAhmedabad621451.02020-07-0417.281Home21526.0004022.6
992T009935693722023-07-170NaNOtherMumbai125053.02019-11-0415.590Electronics42704.0852244.4
993T009941262432022-09-23122.0FemaleVadodara863471.02019-01-2841.311Books2901.8104363.3
994T009954719322022-04-30150.0OtherDelhi1297133.02022-07-3198.821Groceries596.8452253.2
995T009962068512023-12-13063.0OtherMumbai1960377.02022-10-1287.841Electronics28165.6554084.3
996T009977223642022-09-05146.0FemaleMumbai639527.02018-09-274.400Home22807.24153164.6
997T009982204022022-03-11170.0FemaleRajkot1083923.02018-08-1584.771Toys2580.4702343.2
998T009998514012024-05-02061.0FemaleRajkot1006773.02021-06-0730.941Toys2580.4702343.2
999T010005087332022-02-26166.0FemaleDelhi216105.02019-03-25NaN1Groceries1695.89154544.5